A new index for information gain in the Bayesian framework⁎

Jagadeesan, Prem; Raman, Karthik; Tangirala, Arun K.

doi:10.1016/j.ifacol.2020.06.106

Cited by 5 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Fig 3a, we can observe that the change in experimental condition can cure sloppiness, which indicates that sloppiness is a function of information contained in a data set. In our previous work [37], we have demonstrated using simulations that parameters contributing to…”

Section: S ¼ L Min ðXþ L Max ðXþ ð5þmentioning

confidence: 96%

Sloppiness: Fundamental study, new formalism and its application in model assessment

Jagadeesan

Raman²,

Tangirala³

2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges’ crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates’ precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection.

show abstract

Section: S ¼ L Min ðXþ L Max ðXþ ð5þmentioning

confidence: 96%

Sloppiness: Fundamental study, new formalism and its application in model assessment

Jagadeesan

Raman²,

Tangirala³

2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…4b, we can observe that the change in experimental condition can cure sloppiness, which indicates that the sloppiness is a function of information contained in a data set. In our previous work [27], we have demonstrated using simulation that parameters contributing to sloppy directions have very low information gain in the Bayesian framework. For initial condition > 50 of x 2 , the model becomes non-sloppy.…”

Section: Perspectivesmentioning

confidence: 99%

Sloppiness: fundamental study, new formalism and its application in model assessment

Jagadeesan

Raman

Tangirala

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Precise estimation of parameters in a complex dynamical system is often challenging, even if provided with adequate quality and quantity of data. A major challenge is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, its quantification and practical implications in identification still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise a new theoretical definition of sloppiness. Further, we propose a method to quantify sloppiness for non-linear predictors. The proposed method aids in the characterisation of a model structure around a point of interest in the parameter space and detecting local structural unidentifiability. Further, we establish a mathematical relationship between practical identifiability and sloppiness in linear predictors. Finally, we demonstrate the proposed formalism and methods on standard models.

show abstract

“…Information Gain. Information gain [28] is a supervised feature selection methods which is used to rank the feature according to the word's contribution based on its presence or absence in a particular set of text inputs [29]. IG is calculated as…”

Section: Related Workmentioning

confidence: 99%

Relevant‐Based Feature Ranking (RBFR) Method for Text Classification Based on Machine Learning Algorithm

Prasad

Kumar

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

High dimensionality of the feature space is one of the problems in the field of text classification. Identification of optimal subset of features can optimize text classification process in terms of processing time and performance. In this paper, we propose a novel Relevant-Based Feature Ranking (RBFR) algorithm which identifies and selects smaller subsets of more relevant features in the feature space. We compared the performance of the RBFR against other existing feature selection methods such as balanced accuracy measure, information gain, Gini index, and odds ratio on 3 datasets, namely, 20 newsgroup, Reuters, and WAP datasets. We have used 5 machine learning models (SVM, NB, kNN, RF, and LR) to test and evaluate the proposed feature selection method. We found that the performance of the proposed feature selection method is 25.4305% times more effective than the existing feature selection methods in terms of accuracy.

show abstract

A new index for information gain in the Bayesian framework⁎

Cited by 5 publications

References 4 publications

Sloppiness: Fundamental study, new formalism and its application in model assessment

Sloppiness: Fundamental study, new formalism and its application in model assessment

Sloppiness: fundamental study, new formalism and its application in model assessment

Relevant‐Based Feature Ranking (RBFR) Method for Text Classification Based on Machine Learning Algorithm

Contact Info

Product

Resources

About